Must Read Books for Beginners on Machine Learning and Artificial Intelligence

The power to run tasks in automated manner, the power to make our lives comfrotable, the power to improve things continuously by studying decisions at large sacle .

We&#8217;re in the early days, but you&#8217;ll see us in a systematic way think about how we can apply machine learning to all these areas.&#8217;

When Elon Musk, the busiest man of planet right now, was asked about his secret of success, he replied, &#8216;I used to read books.

The motive of this article is not to promote any particular book, but you make you aware of a world which exists beyond video tutorials, blogs and podcasts.

Programming Collective Intelligence, PCI as it is popularly known, is one of the best books to start learning machine learning. If there is one book to choose on machine learning – it is this one.

The book was written long before data science and machine learning acquired the cult status they have today – but the topics and chapters are entirely relevant even today!

Some of the topics covered in the book are collaborative filtering techniques, search engine features, Bayesian filtering and Support vector machines. If you don’t have a copy of this book – order it as soon as you finish reading this article! The book uses Python to deliver machine learning in a fascinating manner.

It has interesting case studies which will help you to understand the importance of using machine learning algorithms.

This book provides a perfect introduction to machine learning. This book prepares you to understand complex areas of machine learning.

This book serves as a excellent reference for students keen to understand the use of statistical techniques in machine learning and pattern recognition.

More than just providing an overview of artificial intelligence, this book thoroughly covers subjects from search algorithms, reducing problems to search problems, working with logic, planning, and more advanced topics in AI such as reasoning with partial observability, machine learning and language processing.

So you love reading but can&#8217;t afford to splurge too much money on books?

We begin the list by going from the basics of statistics, then machine learning foundations and finally advanced machine learning.

One of the stand-out features of this book is it covers the basics of Bayesian statistics as well, a very important branch for any aspiring data scientist.

Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani One of the most popular entries in this list, it&#8217;s an introduction to data science through machine learning. This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field.

Authors: Shai Shalev-Shwartz and Shai Ben-David This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms.

Following that, it covers a list of ML algorithms, including (but not limited to), stochastic gradient descent, neural networks, and structured output learning.

It takes a fun and visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them.

Authors: Anand Rajaraman and Jeffrey David Ullman As the era of Big Data rages on, mining data to gain actionable insights is a highly sought after skill. This book focuses on algorithms that have been previously used to solve key problems in data mining and which can be used on even the most gigantic of datasets.

It starts off by covering the history of neural networks before deep diving into the mathematics and explanation behind different types of NNs.